Academic journal article Environmental Health Perspectives

The Joint Effect of Prenatal Exposure to Metal Mixtures on Neurodevelopmental Outcomes at 20-40 Months of Age: Evidence from Rural Bangladesh

Academic journal article Environmental Health Perspectives

The Joint Effect of Prenatal Exposure to Metal Mixtures on Neurodevelopmental Outcomes at 20-40 Months of Age: Evidence from Rural Bangladesh

Article excerpt

Introduction

Childhood exposure to neurotoxicants is a potential impediment to economic development, as it is most prevalent in developing countries, making this issue particularly poignant in countries such as Bangladesh (Suk et al. 2003; Grandjean et al. 2015). Growing evidence from animal research indicates that the central nervous system is the most vulnerable of all body systems to chemical injury during development (Faustman et al. 2000; Rodier 2004). One of the most widely studied categories of neurotoxicants is metals. Among metals, arsenic, lead, and manganese are prevalent in the environment and have evidence of neurotoxicity. These three metals are thus ideal candidates on which to test new statistical methodologies for mixtures. Arsenic, lead, and manganese exposure is widely prevalent in Bangladesh (Kile et al. 2009), and share the central nervous system as the primary toxicity target in children (Bressler et al. 1999; Clarkson 1987; Polanska et al 2013; Vahter 2008; Zoni and Lucchini 2013). Exposure to lead even at low levels is commonly accepted as neurotoxic. The neurotoxic effects of arsenic and manganese at levels commonly found in the environment are less well understood, but emerging evidence suggests they too are a concern. Prior to our work, manganese-arsenic interaction studies in the Bangladeshi population were cross-sectional and lacked adequate power to assess interactions among mixture components (Wasserman et al. 2006). An epidemiologic investigation in Mexico found evidence of both an inverted "U" relationship between blood Mn and infant development (i.e., both low and high blood Mn levels were associated with poorer performance) and of a lead-manganese interaction being synergistically more toxic (Claus Henn et al. 2010, 2012).

Previous studies on the Bangladeshi population (Wasserman et al. 2004, 2006, 2007, 2008; Hamadani et al. 2011) have shown that arsenic exposure during childhood through drinking water is negatively associated with cognition of school-age children. How-ever, this exposure has not been found to be associated with cognitive development at earlier stages in life (Tofail et al. 2009; Hamadani et al. 2010). A recent study of the independent effect of water manganese exposure among school-age Bangladeshi children exposed to low-level arsenic found evidence of manganese neurotoxicity but no evidence of arsenic effects on neurodevelopment (Wasserman et al. 2006). Our group has recently evaluated, using traditional linear regression approaches, the association between postnatal exposure to heavy metals and Bayley neurodevelopment scores measured at 20-40 mo (Bayley 1993). The analyses were conducted in the same population considered in the present study (Rodrigues et al. 2016). The study reported neurotoxic effect of 24-mo exposure to blood lead and water arsenic, as well as an inverted-U dose-response relationship between water manganese and cognitive development. Recently, more attention has been directed towards studying the joint effects of environmental metal mixtures, that is, investigating interactions that may characterize the joint effect of mixtures (Wright et al. 2006; Claus Henn et al. 2012, 2014). Traditionally, mixtures have been studied via multivariable parametric regression approaches that concomitantly adjust for the confounding effects of mixture components and estimate the independent effect of each component, adjusting for the others. If multiple metals do act as a mixture, this approach would be limited by both multicollinearity and model misspecification. Moreover, it is challenging to specify a correct parametric model that incorporates the possibility of any type of interaction and nonlinear effects among multiple concurrent exposures; the likelihood that all components of a mixture will always have linear effects seems remote. Statistical models designed to address mixtures are relatively new, and several approaches are now available (Bobb et al. …

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